Majority owned EDA

sectors

 [1] "All Industries Total"              "Chemicals"                        
 [3] "Computers and electronic products" "Electrical equipment"             
 [5] "Finance and insurance"             "Food"                             
 [7] "Information"                       "Machinery"                        
 [9] "Mining"                            "Primary and fabricated metals"    
[11] "Professional services"             "Retail Trade"                     
[13] "Total Manufacturing"               "Transportation Equipment"         
[15] "Utilities"                         "Wholesale Trade"                  

glimpse(data)

Rows: 22,470
Columns: 26
$ sector                           <chr> "All Industries Total", "All Industries Total", "All…
$ country                          <chr> "Africa", "Africa", "Africa", "Africa", "Africa", "A…
$ year                             <int> 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007…
$ asset                            <dbl> 38453, 44211, 52398, 60749, 73964, 93415, 109019, 13…
$ compensation_employees           <dbl> 2211, 2332, 2242, 2415, 2745, 3362, 3650, 3994, 4292…
$ employment                       <dbl> 126000, 137200, 139800, 142700, 149800, 154800, 1547…
$ expenditures                     <dbl> 3522, 2765, 4147, 5352, 6793, 8046, 10389, 11172, 11…
$ net_income                       <dbl> 2039, 3981, 2537, 2821, 4740, 7732, 13987, 18154, 21…
$ net_property_plant_and_equipment <dbl> 16140, 16946, 21180, 23297, 29025, 34448, 41078, 478…
$ total_sales                      <dbl> 25539, 34377, 33261, 34070, 40465, 51787, 66438, 791…
$ value_added                      <dbl> 8917, 13785, 12652, 13475, 16861, 22897, 33406, 4233…
$ CI                               <dbl> 16622, 20592, 20609, 20595, 23604, 28890, 33032, 367…
$ Kcca_1                           <dbl> 22313, 27265, 31218, 37452, 44939, 58967, 67941, 836…
$ r                                <dbl> 0.09138171, 0.14601137, 0.08126722, 0.07532308, 0.10…
$ Kcca_2                           <dbl> 181896.4, 141030.1, 253595.5, 273422.2, 223784.8, 22…
$ Kva                              <dbl> 24195.21, 15971.36, 27588.00, 32061.89, 26024.80, 25…
$ TG                               <dbl> 0.032546817, 0.066148952, 0.031718051, 0.030395141, …
$ TGstock                          <dbl> 0.12633209, 0.23492270, 0.11978281, 0.12108855, 0.16…
$ TGasset                          <dbl> 0.05302577, 0.09004546, 0.04841788, 0.04643698, 0.06…
$ PT                               <dbl> 0.07076984, 0.10047376, 0.09050072, 0.09442887, 0.11…
$ Rem                              <dbl> 0.001462302, 0.001416424, 0.001336433, 0.001410301, …
$ COtec                            <dbl> 0.12809524, 0.12351312, 0.15150215, 0.16325858, 0.19…
$ COv                              <dbl> 7.299864, 7.266724, 9.446922, 9.646791, 10.573770, 1…
$ TP                               <dbl> 0.9222071, 1.7071184, 1.1315789, 1.1681159, 1.726776…
$ TPr                              <dbl> 0.084272862, 0.249258689, 0.091960273, 0.087986091, …
$ Continent                        <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", …

Stock de capital invertido

TG

data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total", "Asia and Pacific") ) %>%
  filter( !sector %in% c("Finance without depository", "Finance and insurance") ) %>%
  ggplot(aes(year, TGstock, color = country)) +
  geom_line(size = 0.75, alpha = 0.75)+
  facet_wrap(~sector)+ #scales = "free"
  scale_y_continuous(labels = scales::percent)+
  theme_minimal()+
  labs(title= "Tasa de ganancia (sin rotación) de inversiones de EEUU", subtitle = "Europa y América del Sur")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, size= axis_size),
        axis.text.y = element_text(size= axis_size),
        axis.title  = element_blank() ,
        strip.text = element_text(size=strip_size))+
  scale_color_manual(values=wes_palette(n=4, name="Moonrise2")) #"Royal2"
ggsave("./results/bea/majority_owned_nonbank/tg_eu_sa_all_2.png")

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data %>%
  filter( Continent %in% c("South America") ) %>%
  filter( !sector %in% c("Finance without depository", 
                         "Finance and insurance",
                         "Professional services",
                         "Wholesale Trade") ) %>%
  ggplot(aes(year, TGstock, color = country)) +
  geom_line()+
  facet_wrap(~sector)+ #scales = "free"
  scale_y_continuous(labels = scales::percent)+
  theme_minimal()+
  labs(title= "Tasa de ganancia (sin rotación) de inversiones de EEUU", subtitle = "América del Sur")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        legend.text = element_text(size = axis_size+6),
        axis.text.x = element_text(angle = 45, size= axis_size+6),
        axis.text.y = element_text(size= axis_size+6),
        axis.title  = element_blank() ,
        strip.text = element_text(size=strip_size+5))+
  scale_color_brewer(palette="Paired")

Warning: Removed 53 rows containing missing values (`geom_line()`).

ggsave("./results/bea/majority_owned_nonbank/tg_sa_2.png", width = 15, height=10)

Warning: Removed 53 rows containing missing values (`geom_line()`).

data %>%
  filter( Continent %in% c("South America") | country %in% c("All Countries Total",
                                                             "European Union") ) %>%
  ggplot(aes(year, TGstock, color = country)) +
  geom_line()+
  facet_wrap(~sector, scales = "free")+
  scale_y_continuous(labels = scales::percent)+
  theme_minimal()+
  labs(title= "Tasa de ganancia (sin rotación) de inversiones de EEUU", subtitle = "América del Sur, Europa y total paises")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, size= 5),
        axis.text.y = element_text(size= 5),
        axis.title  = element_blank() ,
        strip.text = element_text(size=10))+
  scale_color_brewer(palette="Paired")
ggsave("./results/bea/majority_owned_nonbank/tg_sa_eu_all.png", width = 15, height=10)

data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total") ) %>%
  group_by(country, sector) %>% 
  summarise(TGstock = mean(TGstock, na.rm=T) , 
            PT = mean(PT, na.rm=T)) %>% 
  ggplot(aes(country, TGstock, fill = country)) +
  geom_col(position = "dodge")+
  facet_wrap(~sector, scales = "free")+
  scale_y_continuous(labels = scales::percent_format(accuracy = 1L))+
  theme_minimal()+
  labs(title= "Tasa de ganancia (sin rotación) de inversiones de EEUU", subtitle = "América del Sur, Europa y total países (promedio 1999-2019)")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size= 8),
        axis.title  = element_blank() ,
        strip.text = element_text(size=5))+
  scale_fill_manual(values=wes_palette(n=3, name="Royal2"))

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

ggsave("./results/bea/majority_owned_nonbank/tg_eu_sa_all_avg.png")

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Productivity (PT)

data_prod <- data %>%
    select(c(sector, country, Continent , year,value_added, employment, PT)) %>%
    filter(PT > 0 &  employment > 0 ) %>%
    arrange(desc(PT)) #    arrange(PT)

glimpse(data_prod)

Rows: 16,857
Columns: 7
$ sector      <chr> "Mining", "Wholesale Trade", "Wholesale Trade", "Wholesale Trade", "Mining", "Wholesale Trade", "Mining"…
$ country     <chr> "Denmark", "Barbados", "Barbados", "Barbados", "Denmark", "United Kingdom Islands, Caribbean", "Denmark"…
$ Continent   <chr> "Europe", "Central America and the Caribbean", "Central America and the Caribbean", "Central America and…
$ year        <int> 2008, 2008, 2006, 2011, 2011, 2007, 2012, 2004, 2010, 2005, 2006, 2010, 2008, 2010, 2003, 2005, 2013, 20…
$ value_added <dbl> 7326, 3032, 2295, 2142, 5671, 1776, 4741, 1556, 4658, 1430, 1372, 2579, 2486, 1237, 1203, 1172, 4615, 22…
$ employment  <dbl> 200, 100, 100, 100, 300, 100, 300, 100, 300, 100, 100, 200, 200, 100, 100, 100, 400, 200, 100, 300, 200,…
$ PT          <dbl> 36.630000, 30.320000, 22.950000, 21.420000, 18.903333, 17.760000, 15.803333, 15.560000, 15.526667, 14.30…

head(data_prod)

top_countries_by_year <- data_prod %>%
  select(-c(Continent , value_added, employment)) %>% 
  group_by(year) %>%
  top_n(3, PT) %>% 
  arrange(-PT)
top_countries_by_year

top_countries_by_year <- data_prod %>%
  select(-c(Continent , value_added, employment)) %>% 
  group_by(year) %>%
  top_n(3, PT) %>% 
  arrange(-PT)
top_countries_by_year

data_prod %>%
  filter( country %in% c("South America", "Europe") ) %>%
  ggplot(aes(year, PT/10e3, fill = country)) +
  geom_col(position = "dodge")+
  facet_wrap(~sector, scales = "free")+
  theme_minimal()+
  labs(title= "Productividad de inversiones de EEUU", subtitle = "Total Europa y América del Sur",
       y = "Miles de USD por obrero")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, size= 5),
        axis.text.y = element_text(size= 5),
        axis.title.x  = element_blank() ,
        axis.title.y  = element_text(size = 3.9) ,
        strip.text = element_text(size=3.9))+
  scale_fill_manual(values=wes_palette(n=3, name="Royal1"))
ggsave(paste0(results_path,"pt_eu_sa.png"))

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# Iterate through each sector
for (sec in sectors) {
  # Subset the data for the current sector
  sector_data <- data_prod %>%
    filter(sector == sec) 
  
  # Create the plot for the current sector
  plot <- ggplot(sector_data, aes(year, PT/10e3, color = country)) +
    geom_line() +
    facet_wrap(~Continent) + #scales = "free"
    theme_minimal() +
    labs(title = paste("Productividad en", sec, sep = " "),
         subtitle = paste("Total paises por contiente", sec),
         y = "Miles de USD por obrero") +
    theme(legend.position = "none",
          legend.title = element_blank(),
          axis.text.x = element_text(angle = 45, size = 5),
          axis.text.y = element_text(size = 5),
          axis.title.x = element_blank(),
          axis.title.y = element_text(size = 3.9),
          strip.text = element_text(size = 3.9)) 
  print(ggplotly(plot))
  ggsave(filename = paste(results_path, "plot_pt_", sec, ".png", sep = ""),
         plot = plot)
    
  
}

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`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?

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data_prod %>%
  filter( Continent %in% c("South America") ) %>%
  left_join(data %>% 
              filter(country == "Europe") %>% 
              select(year, sector, PTeu=PT) ,
            by = c("year", "sector" )) %>%
  mutate(PTrel = PT/PTeu ) %>% 
  ggplot(aes(year, PTrel, color = country)) +
  geom_line(size = 0.3)+
  facet_wrap(~sector, scales = "free")+
  scale_y_continuous(labels = scales::percent)+
  theme_minimal()+
  labs(title= "Brecha de productividad de inversiones de EEUU", 
       subtitle = "América del Sur relativa a total Europa")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, size= 5),
        axis.text.y = element_text(size= 4),
        axis.title  = element_blank()  ,
        strip.text = element_text(size=5))+
  scale_color_brewer(palette="Paired")
ggsave("./results/bea/majority_owned_nonbank/pt_eu_sa_relativa.png")

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## Nivel de productividad (todos los sectores)
data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total") ) %>%
  group_by(country, sector) %>% 
  summarise(TGstock = mean(TGstock, na.rm=T) , 
            PT = mean(PT/10e3, na.rm=T)) %>% 
  ggplot(aes(country, PT, fill = country)) +
  geom_col(position = "dodge")+
  facet_wrap(~sector, scales = "free")+
  theme_minimal()+
  labs(title= "Productividad de inversiones de EEUU", 
       subtitle = "América del Sur, Europa y total países (promedio 1999-2019)",
       y = "Miles de USD por obrero")+
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size= 8),
        axis.title.x  = element_blank() ,
        axis.title.y  = element_text(size = 5) ,
        strip.text = element_text(size=5))+
  scale_fill_manual(values=wes_palette(n=3, name="Moonrise3"))

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

ggsave("./results/bea/majority_owned_nonbank/pt_eu_sa_all_avg.png")

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## Nivel de productividad (sectores seleccionados)
data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total") &
            sector %in% c("All Industries Total","Mining", "Transportation Equipment", "Total Manufacturing"        )) %>%
  group_by(country, sector) %>% 
  summarise(TGstock = mean(TGstock, na.rm=T) , 
            PT = mean(PT/10e3, na.rm=T)) %>% 
  ggplot(aes(country, PT, fill = country)) +
  geom_col(position = "dodge")+
  facet_wrap(~sector, scales = "free")+
  theme_minimal()+
  labs(title= "Productividad del trabajo de inversiones de EEUU", 
       subtitle = "América del Sur, Europa y total países (promedio 1999-2019)",
       y = "Miles de USD por obrero")+
  theme(plot.title = element_text(size= title_size),
        plot.subtitle = element_text(size= title_size*.8),
        legend.position = "bottom",
        legend.title = element_blank(),
        legend.text = element_text(size=text_size),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size= text_size),
        axis.title.x  = element_blank() ,
        axis.title.y  = element_text(size = text_size) ,
        strip.text = element_text(size=text_size))+
  scale_fill_manual(values=wes_palette(n=3, name="Moonrise3"))

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

ggsave("./results/bea/majority_owned_nonbank/pt_eu_sa_all_avg_sectors.png", width = 15, height=10)

TG y PT

data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total",
                         "Asia and Pacific", "Central America", "Mexico") ) %>% 
  filter( sector %in% c("All Industries Total","Mining", 
                        "Transportation Equipment", "Total Manufacturing"  ) ) %>% 
  group_by(country, sector) %>% 
  summarise(TGstock = mean(TGstock, na.rm=T) , 
            PT = mean(PT/10e3, na.rm=T)) %>% 
  reshape2::melt() %>% 
  ggplot(aes(x=reorder(variable,-value),y= value, fill = country)) +
  # geom_col(position = "dodge")+
  geom_bar(position = "dodge", stat="identity")+
  facet_wrap(sector~variable,
             # scales = "free",
             ncol=2
             # ,strip.position = c("left", "top")
             # labeller = as_labeller(c(TGstock = "ratio TG", PT = "Miles de USD por obrero") )
             )+
  theme_minimal()+
  labs(title= "Productividad del trabajo y TG de inversiones de EEUU", 
       subtitle = "Promedio 1999-2019", y="Miles de USD por obrero y ratio TG")+
  theme(plot.title = element_text(size= 12),
        plot.subtitle = element_text(size= 12*.8),
        legend.text = element_text(size=7),
        legend.position = "bottom",
        legend.title = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_text(size= 10),
        axis.text.y = element_text(size= 7),
        axis.text.x = element_blank(),
        strip.text = element_text(size=10),
        strip.placement = "outside" )+
  scale_fill_brewer(palette="Paired")

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

Using country, sector as id variables

Salario

data %>%
  filter( country %in% c("South America", "European Union", "All Countries Total",
                         "Asia and Pacific", "Central America", "Mexico") ) %>% 
  filter( sector %in% c("All Industries Total","Mining", 
                        "Transportation Equipment", "Total Manufacturing"  ) ) %>% 
  group_by(country, sector) %>% 
  summarise(Rem = mean(Rem*10e6, na.rm=T) ) %>% 
  reshape2::melt() %>% 
  ggplot(aes(x=reorder(variable,-value),y= value, fill = country)) +
  # geom_col(position = "dodge")+
  geom_bar(position = "dodge", stat="identity")+
  facet_wrap(~sector, scales = "free", ncol=2)+
  theme_minimal()+
  labs(title= "Salario promedio en las inversiones de EEUU", 
       subtitle = "Promedio 1999-2019", y="USD")+
  theme(plot.title = element_text(size=title_size),
        plot.subtitle  = element_text(size=title_size*.8),
        legend.position = "bottom",
        legend.title = element_blank(),
        legend.text = element_text(size=text_size),
        axis.title.x = element_blank(),
        axis.title.y = element_text(size= text_size),
        axis.text.y = element_text(size= text_size),
        axis.text.x = element_blank(),
        strip.text = element_text(size=text_size))+
  scale_fill_brewer(palette="Paired")

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

Using country, sector as id variables

ggsave("./results/bea/majority_owned_nonbank/salario_avg_sectors.png", width = 15, height = 10)